Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image

Abstract

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p = 0.000 < 0.05 . Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p = 0.128 > 0.05 ). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 ( p < 0.05 ). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.

Ultrasonic Image Features under the Intelligent Algorithm in the Diagnosis of Severe Sepsis Complicated with Renal Injury

“…Studies showed that ultrasound diagnosis technology has high accuracy as an auxiliary method for the diagnosis of kidney diseases. Determination of the renal blood flow resistance index (RI) can reflect renal tubular necrosis in patients, which has a clinical application value for AKI diagnosis [11][12][13].…”

Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide

“…The cluster analysis of high-frequency keywords and references can identify the hot spots and frontiers of machine learningrelated AKI research, among which "prognosis", "sepsis", "critically patients", "MIMIC", and "XGBoost" are the hot spots of research based on the time overlay map. The current research on machine learning in sepsis-related AKI mainly includes two aspects: one is the study of constructing predictive models (37), and the other is the auxiliary addition to the analysis and processing of ultrasound images of AKI (8,38). In several studies, the diagnosis and treatment effect of sepsis-related AKI has been greatly improved by introducing XGBoost, deep learning and neural network algorithms (8,37,38), but its effectiveness in clinical practice remains to be confirmed.…”

“…The current research on machine learning in sepsis-related AKI mainly includes two aspects: one is the study of constructing predictive models (37), and the other is the auxiliary addition to the analysis and processing of ultrasound images of AKI (8,38). In several studies, the diagnosis and treatment effect of sepsis-related AKI has been greatly improved by introducing XGBoost, deep learning and neural network algorithms (8,37,38), but its effectiveness in clinical practice remains to be confirmed. HA-AKI in critically ill patients has always been one of the research hotspots in this field (39)(40)(41)(42).…”

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

“…Ying et al [ 250 ] proposed a PCNN method for the diagnosis of severe sepsis complicated by AKI using an ultrasonic image. Their study explains their CNN-based ultrasonic image enhancement technique, which was later compared with the histogram equalization and linear transformation algorithms.…”